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The Evolution of Okun’s Law and of Cyclical Productivity Fluctuations in the United States and in the EU-15

The Evolution of Okun’s Law and of Cyclical Productivity Fluctuations in the United States and in the EU-15. Robert J. Gordon Northwestern, NBER, CEPR, OFCE Seminar Presentation at OFCE Paris, September 14, 2011. Themes of Paper with Broader Implications.

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The Evolution of Okun’s Law and of Cyclical Productivity Fluctuations in the United States and in the EU-15

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  1. The Evolution of Okun’s Law and of Cyclical Productivity Fluctuations in the United States and in the EU-15 Robert J. Gordon Northwestern, NBER, CEPR, OFCE Seminar Presentation at OFCE Paris, September 14, 2011

  2. Themes of Paper with Broader Implications Procyclical productivity shocks are not a fundamental object in macroeconomics; they are residual artifacts of lagging hours Productivity’s lead does not prove causation The productivity residual varies across time and places (US vs EU) as a result of labor-market institutions. Procyclical productivity fluctuations have nothing to do with technology, and the phrase “technology shocks” should be banished from macroeconomics

  3. Further Broad Themes EU vs. US differences in cyclicality of productivity reflects American Exceptionalism and European institutions Among these are Rising inequality in US, shift in power from labor to management Much less increase in EU inequality (outside of UK), corporatist constraint on executive compensation. Germany management and union agreement on work sharing and kurzarbeit. Long history of EU corporatism and government policy to share work by reducing hours

  4. Changes in Cyclical Labor-market Behavior Point of Departure: Okun’s Law (1962) In response to a 1 percent change in the output gap, procyclical responses of hours 2/3, of which employment 1/3, LFPR 1/6, hours/employment 1/6 Procyclical productivity fluctuations make up remaining 1/3 US study: A new approach to detrending data Contrasts H-P and Kalman filters uses “outside information” from inflation to determine the unemployment rate gap Feeds U gap into Kalman filter to eliminate cyclical component of trend in output and aggregate hours.

  5. Other New Findings: Unconventional US Data and Analysis of EU-15 Quarterly Data For US only: a new approach to data US: Total Economy not NFPB Sector US: Conventional vs. Unconventional Measures For EU, an attempt to create quarterly data for EU-15 aggregate that duplicate those long available for US First quarterly data series back to 1963 on employment and output with consistent aggregation Merged post-2000 quarterly hours with earlier annual hours data to create a series back to 1970 Main finding: in the US, productivity no longer exhibits procyclical fluctuations. But in the EU, productivity is still procyclical by about the same amount as before. A key finding for the US: hours gap ~= output gap in 2008-09. But as predicted by Okun’s Law, hours gap < output gap in US in 1980-82 and in EU-15 for 08-09.

  6. Preview of Substantive Hypotheses to Explain Changes Joint explanation of US and EU behavior based on “American Exceptionalism” US shift toward greater labor input response is explained by the “Disposable Worker” hypothesis Increased managerial power, new emphasis on maximizing shareholder value, decreased power of labor groups and employees

  7. Preview of Substantive Hypotheses (Continued) Europe has not experienced a parallel shift in market power between labor and management Also, several important EU countries have developed institutions and policies that explicitly or implicitly restrict the responsiveness of labor to output changes, e.g. “work sharing” These policies shift the impact of output changes from employment level onto hours per employee and consequently output per employee Work sharing in Europe in the form of shorter hours per employee does not show up in our results

  8. Simple Version and Conventional U. S. Data Version for the Total Economy

  9. Introducing the Alternative “Unconventional” Identity • Nalewaik’s 2010 Brookings Paper: • GDP and GDI are conceptually identical • But they differ (statistical discrepancy) • GDI is more procyclical • When GDP is revised, it tends to be revised toward what GDI already shows • Hours • All existing work uses hours based on payroll employment • There is a little-known series on hours based on the household survey • In principle 2 numerators, 2 denominators = 4 possible productivity measures, here we simplify by comparing only two combinations, Conventional and Unconventional

  10. Conventional Compared to Unconventional Identity

  11. First Part of Paper: Detrending the Full-Period US Data Uses Kalmandetrending, which allows use of an outside feedback variable. Avoids excessive cyclicality of H-P trends For this outside information, turn to a technique for estimating the time-varying natural rate of unemployment (TV-NAIRU) When possible, we prefer to use Kalman over HP or Bandpass filters, which use only a univariate series to detrend itself. Last part of paper: study of US vs. EU redoes US results in a restricted format to match data availability for EU. Uses H-P filter as a stopgap variable for both US and EU.

  12. TV-NAIRU: The Kalman Feedback Variable The TV-NAIRU provides the outside information for the Kalman trends Calculated through an established procedure of regressing the growth of inflation on lagged inflation, unemployment, and supply shock variables. The standard NAIRU is too volatile to plausibly represent trend employment, so we take a 20-quarter centered moving average The time period of 2008-11 distorts the entire NAIRU, because there is a large output gap without a steady decline in core inflation We cut off the NAIRU at its 2005:Q1 value of 5.0 percent This is consistent with the subsequent decision to cut off changes in estimated trends at 2007:Q4 in both the U. S. and Europe

  13. The Unemployment Gap, Fed Back to the Kalman Trend

  14. The Wild Differences inHours Trends

  15. Equally Sharp Differencesin Output Trends

  16. Alternative Productivity Trends

  17. Special Problem Posed by 2008-11 Cycle Hours and employment gaps for the US respond roughly as much as output gap The unemployment gap drives the trend adjustment which treats the entire post-1954 interval as homogeneous Estimated through 2011:Q2, the Kalman procedure thinks that the long term trend hours growth must be implausibly low to generate the observed decline in hours We avoid making judgments on 2008-11 cycle by constraining all growth trends as equal to 2007:Q4 growth rates throughout 2008-11 Thus the paper “dodges” the hot current (as yet unanswerable) topic of the new normal

  18. Conventional (C) vs. Unconventional (U): Medium-run Growth Trends Major findings in Table 1 The mysterious upsurge in LP growth 2001-07 in C data does not exist in U data Big differences in AAGR of LP growth Conventional 96-01: 2.11, 01-07: 2.10 Unconventional 96-01: 2.33, 01-07: 1.24 This supports the view that the late 1990s US productivity revival was a one-shot event, not a permanent change in the trend

  19. Kalman Trends: Conv vs. Unconv Output & Hours

  20. Unconventional Productivity: New Story for 1994-2007

  21. Basis of U. S. Analysis: Kalman Trends for Conventional Definitions

  22. Continuing with U. S.-Only Data:What We Learn from Cyclical Deviations from Trend (“Gaps”) The most interesting results Okun’s 2/3 hours vs. 1/3 productivity result worked perfectly in late 1960s and early 1980s but at almost no other time The 2008-09 cycle has been as big for hours than for output, while 1980-82 saw a much larger response of output than hours Correlation of productivity gap with output gap changes timing and disappears after mid-1980s

  23. US: Gaps for C & U Average: Output, Hours, Productivity

  24. US: Gaps for Three Components of Aggregate Hours

  25. Regression Analysis for US-Only 1954-2011 All variables expressed as QUARTERLY FIRST DIFFERENCES OF DEVIATION FROM TREND, i.e. Δ log gap in X Changes in gaps for output identity components explained by Changes in output gap (current, four lags, and four leads) Lagged dependent variable (lags 1-4) Error correction term Interactive dummy on output gap (for full period) End-of-expansion dummies (7)

  26. End of Expansion Variable End of Expansion Effect Productivity slows late in expansion, hours grow too fast (“overhiring”) Constrained to be completely offset by faster productivity growth early in recovery (“Early Recovery Productivity Bubble” Implementation Not 0,1 dummies. They enter in the form 1/M, -1/N M is the number of quarters in late expansion, N in early recovery These sum to zero

  27. Error Correction Term Error Correction Term Linked to the concept of cointegration Informal Definition: A linear combination of two series is stationary. See Engle and Granger (1987) A regression using only differenced data is misspecified, and one using only levels will ignore important constraints. Solution: Add an error correction variable to the regression consisting of the lagged log ratio of the gdp gap to the dependent variable gap, allowing for separate coefficients on the numerator and denominator For further reference on the concept of error correction in a bivariate model, see Hendry, Pagan, and Sargan (1984)

  28. Regression Results for US-Only, 1954-86 vs. 1986-2011 Hours gap lags output by roughly one quarter Productivity leads output by roughly two and half quarters End-of-expansion dummies (8 recessions) To simplify tables, constrained to be equal within subsample Significant in Hours and LP equations pre 1986 and in full-period regressions Split sample: 1954-86 vs 1986-2011 Big change in long-run responses They don’t pass Chow tests, which are too demanding Interactive dummy shows a statistically significant change in the sum of the output coefficients To simplify paper, regressions are presented only for conventional concept of hours & LP Unconventional data are noisier due to household hours

  29. US: Long-Run Responses, Before and After 1986

  30. Implications of Regression Analysis Okun’s Law is overturned, Hours now respond by >1 to output deviations, not <1 Productivity no longer responds procyclically to output fluctuations No more Okun’s Law No more SRIRL No more RBC No more procyclical “technology shocks,” i.e., productivity fluctuations as exogenous inputs in DSGE and other modern macro theories

  31. The “Early Recovery Productivity Bubble” On average since 1970 LP has grown 1.4 percent AAGR faster than trend in first four quarters of recovery 0.00 percent faster in following eight quarters 2002-03 was unusual because fast growth continued in the subsequent 8 quarters (but not in the unconventional data) EOE effect explains about 2/3 of first four quarters

  32. Actual and Fitted, Early and Late Equations for Hours

  33. Actual and Fitted, Early and Late Equations for Productivity

  34. Now We Turn toComparison of US and EU • The motivating puzzle, shown on the next slide, is that conditional on the decline in output, U rate increased more in U. S. than in EU • To avoid keeping track of 15 countries, we study only the EU-15 aggregate • Some of data are for Euro Area, not EU-15 • Reason horizontal axis is change in output, not output gap (OECD gaps are implausible)

  35. Relationship of Unemployment and Output in US and EU • The first graph shows the relationship between the cumulative growth rates of output and unemployment in the recession. • The US is an outlier: the increase in unemployment is higher than the decrease in output would predict • Next Figure: The U.S. 2010 level of unemployment is not unusually high; 2007 unemployment was unusually low

  36. US vs EU: Cumulative Change of Output and Unemployment 07-10

  37. US vs EU: Level of Unemployment vs. Growth of Output

  38. Stripped Down Identity for Comparing US and European Data • No suitable quarterly data (yet) for the EU on Employment Rate or LFPR • Y/H: Output per Hour: labor productivity • H/E and E/N: Components of aggregate hours, the labor input • H/E problem, a hybrid between payroll survey and household survey

  39. Comparing the US and the EU, Graphs and Regressions Uses simplified output identity: only two components of aggregate hours: H/E and E/N Other differences from full US regression No EOE variable (not available for Europe) Shorter time period, 1971:Q2 – 2011:Q1 Early = 1971-1991, Late = 1991-2011 No outside cyclical variable, so we can’t use Kalman detrending, instead we use Hodrick-Prescott filter with a parameter of 6400, running the trends to 2007:Q4 and extending the trend growth rates to 2011:Q1

  40. US vs EU: Actual Four-Quarter Growth of Output

  41. Observations on the Actual Growth Rates in US and EU • Change in output growth from 2008-2010 is nearly identical for EU and US • Between 1986 and 2006 volatility of output growth is about the same • Pre-1986 the US has a much more volatile business cycle with back-to-back recessions in 1980 and 1981-82

  42. Observations on the Trend Growth Rates in US and EU European productivity trend growth starts high (4.5 percent), then steadily declines Difference between the productivity trends is especially high after 1994, when US LP begins to increase steeply The growth disparity after 1994 has its counterpart in levels: PPP-adjusted productivity in the EU relative to the US reached 92 percent in 1995 then slipped back to about 83 percent

  43. US vs EU: Trend Four-Quarter Growth of Output, Hours

  44. US vs EU: Trend Four-Quarter Growth of Labor Productivity

  45. US vs EU: Trend Four-Quarter Growth of H/E, E/N

  46. Now Come Graphs of Actual vs. Trend Changes • The next two charts show the division of the hours response between Hours per employee (H/E) and employment per capita (E/N). • Notice the lack of a strong difference between US and EU regarding E/H • A much more visible difference in response of E/N

  47. EU vs. US Actual vs. Trend Hours per Employee

  48. EU vs. US Actual vs. Trend Employment per Capita

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